cm007 Exercises: Exploring Aesthetic Mappings

library(tidyverse)

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Beyond the x and y aesthetics

Switch focus to exploring aesthetic mappings, instead of geoms.

Shapes

• Try a scatterplot of gdpPercap vs pop with a categorical variable (continent) as shape.

• As with all (?) aesthetics, we can also have them not as aesthetics!
  • Try some shapes: first as integer from 0-24, then as keyboard characters.
  • What’s up with pch?

List of shapes can be found at the bottom of the scale_shape documentation.

Colour

Make a scatterplot. Then:

• Try colour as categorical variable.
• Try colour and color.
• Try colour as numeric variable.
  • Try trans="log10" for log scale.

Make a line plot of gdpPercap over time for all countries. Colour by lifeExp > 60 (remember that lifeExp looks bimodal?)

Try adding colour to a histogram. How is this different?

Facetting

Make histograms of gdpPercap for each continent. Try the scales and ncol arguments.

Remove Oceania. Add another variable: lifeExp > 60.

Bubble Plots

• Add a size aesthetic to a scatterplot. What about cex?
• Try adding scale_radius() and scale_size_area(). What’s better?
• Use shape=21 to distinguish between fill (interior) and colour (exterior).

“Complete” plot

Let’s try plotting much of the data.

• gdpPercap vs lifeExp with pop bubbles
• facet by year
• colour by continent

Continue from last time (geom exploration with x and y aesthetics)

Path plots

Let’s see how Rwanda’s life expectancy and GDP per capita have evolved over time, using a path plot.

• Try geom_line(). Try geom_point().
• Add arrow=arrow() option.
• Add geom_text, with year label.

Two categorical variables

Try cyl (number of cylinders) ~ am (transmission) in the mtcars data frame.

• Scatterplot? Jitterplot? No.
• geom_count().
• geom_bin2d(). Compare with geom_tile() with fill aes.

Overplotting

Try a scatterplot with:

• Alpha transparency.
• geom_hex()
• geom_density2d()
• geom_smooth()

Bar plots

How many countries are in each continent? Use the year 2007.

1. After filtering the gapminder data to 2007, make a bar chart of the number of countries in each continent. Store everything except the geom in the variable d.

2. Notice the y-axis. Oddly, ggplot2 doesn’t make it obvious how to change to proportion. Try adding a y aesthetic: y=..count../sum(..count..).

Uses of bar plots: Get a sense of relative quantities of categories, or see the probability mass function of a categorical random variable.

Polar coordinates

• Add coord_polar() to a scatterplot.

Want more practice?

If you’d like some practice, give these exercises a try

Exercise 1: Make a plot of year (x) vs lifeExp (y), with points coloured by continent. Then, to that same plot, fit a straight regression line to each continent, without the error bars. If you can, try piping the data frame into the ggplot function.

Exercise 2: Repeat Exercise 1, but switch the regression line and geom_point layers. How is this plot different from that of Exercise 1?

Exercise 3: Omit the geom_point layer from either of the above two plots (it doesn’t matter which). Does the line still show up, even though the data aren’t shown? Why or why not?

Exercise 4: Make a plot of year (x) vs lifeExp (y), facetted by continent. Then, fit a smoother through the data for each continent, without the error bars. Choose a span that you feel is appropriate.

Exercise 5: Plot the population over time (year) using lines, so that each country has its own line. Colour by gdpPercap. Add alpha transparency to your liking.

Exercise 6: Add points to the plot in Exercise 5.